Satellite signal based positioning technologies, which are mainly used outdoors, are usually not suited to deliver a satisfactory performance when used for indoor positioning, since satellite signals of global navigation satellite systems (GNSS), like the global positioning system (GPS), do not penetrate through walls and roofs strongly enough for an adequate signal reception indoors. Thus, these positioning technologies are not able to deliver a performance indoors that would enable seamless, equal and accurate navigation experience outdoors and indoors.
Therefore, several dedicated solutions for indoor positioning have been developed and commercially deployed during the past years. Examples comprise solutions that are based on pseudolites, which are ground based GPS-like short-range beacons, ultra-sound positioning solutions, Bluetooth low energy (BTLE) based positioning solutions, and wireless local area network (WLAN) based positioning solutions.
Indoor positioning requires novel systems and solutions that are specifically developed and deployed for this purpose. The “traditional” positioning technologies which are used mainly outdoors, i.e. satellite and cellular positioning technologies, cannot deliver such a performance indoors that would enable seamless and equal navigation experience in both environments. The required positioning accuracy (2-3 m), coverage (˜100%) and floor detection are challenging to achieve with satisfactory performance levels with the systems and signals that were not designed and specified for the indoor use cases in the first place. Satellite-based radio navigation signals simply do not penetrate through the walls and roofs strongly enough for the adequate signal reception and the cellular signals have too narrow bands for accurate ranging by default.
Several indoor-dedicated solutions have already been developed and commercially deployed during the past years e.g. solutions based on pseudolites (GPS-like short-range beacons), ultra-sound positioning, BTLE signals (e.g. Nokia High-Accuracy Indoor Positioning, HAIP) and WiFi fingerprinting. What is typical to these solutions is that they require either deployment of totally new infrastructure (beacons, tags and so on) or manual exhaustive radio surveying of the buildings including all the floors, spaces and rooms. This is rather expensive and will take a considerable amount of time to build the coverage to the commercially expected level, which in some cases narrowed the potential market segment only to very thin customer base e.g. for health care or dedicated enterprise solutions. Also, the diversity of these technologies makes it difficult to build a globally scalable indoor positioning solution, and the integration and testing will become complex if a large number of technologies is needed to be supported in the consumer devices, such as smartphones.
For an indoor positioning solution to be commercially successful, that is, 1) being globally scalable, 2) having low maintenance and deployment costs, and 3) offering acceptable end-user experience, the solution needs to be based on an existing infrastructure in the buildings and on existing capabilities in the consumer devices. This leads to an evident conclusion that the indoor positioning needs to be based on WiFi- and/or Bluetooth (BT)-technologies that are already supported in the every smartphone, tablet, laptop and even in the majority of the feature phones. It is now needed to find a solution that uses the WiFi- and BT-radiosignals in such a way that makes it possible to achieve 2-3 m horizontal positioning accuracy, close to 100% floor detection with the ability to quickly build the global coverage for this approach.
Huge volumes of indoor WiFi-measurements data could be harvested via crowd-sourcing if the consumer devices were equipped with the necessary functionality to enable the WiFi-data collection as a background process, naturally with the end-user consent. It could also be possible to use volunteers to survey the sites (buildings) in exchange of reward or recognition and get the coverage climbing up globally in the places and venues important for the key customers. However, the technical challenges related to the harvesting, processing, redundancy, ambiguity and storing the crowd-sourced data need to be understood and solved first, before the Wifi-radiomap creation can be based on the machine learning of the indoor WiFi-radiomaps.